


def 	lowerCamelCase_							(       UpperCAmelCase__							,     UpperCAmelCase__   ):



						"""simple docstring"""




						a_		      =		word.split()

						def justify(UpperCAmelCase__							,     UpperCAmelCase__							,     UpperCAmelCase__   ) -> str:
												a_		      =		max_width - width
												a_		      =		len(_lowerCamelCase   )
												if len(_lowerCamelCase   ) == 1:
																		# if there is only word in line
																		# just insert overall_spaces_count for the remainder of line
																		return line[0] + " " * overall_spaces_count
												else:
																		a_		      =		words_count - 1
																		# num_spaces_between_words_list[i] : tells you to insert
																		# num_spaces_between_words_list[i] spaces
																		# after word on line[i]
																		a_		      =		spaces_to_insert_between_words * [
																		    overall_spaces_count // spaces_to_insert_between_words
																		]
																		a_		      =		(
																		    overall_spaces_count % spaces_to_insert_between_words
																		)
																		# distribute spaces via round robin to the left words
																		for i in range(_lowerCamelCase   ):
																								num_spaces_between_words_list[i] += 1
																		a_		      =		[]
																		for i in range(_lowerCamelCase   ):
																								# add the word
																								aligned_words_list.append(line[i]   )
																								# add the spaces to insert
																								aligned_words_list.append(num_spaces_between_words_list[i] * """ """   )
																		# just add the last word to the sentence
																		aligned_words_list.append(line[-1]   )
																		# join the aligned words list to form a justified line
																		return "".join(_lowerCamelCase   )

						a_		      =		[]
						a_		      =		[]
						a_		      =		0
						for word in words:
												if width + len(_lowerCamelCase   ) + len(_lowerCamelCase   ) <= max_width:
																		# keep adding words until we can fill out max_width
																		# width = sum of length of all words (without overall_spaces_count)
																		# len(word) = length of current word
																		# len(line) = number of overall_spaces_count to insert between words
																		line.append(_lowerCamelCase   )
																		width += len(_lowerCamelCase   )
												else:
																		# justify the line and add it to result
																		answer.append(justify(_lowerCamelCase							,     _lowerCamelCase							,     _lowerCamelCase   )   )
																		# reset new line and new width
																		a_      , a_		      =		[word], len(_lowerCamelCase   )
						a_		      =		max_width - width - len(_lowerCamelCase   )
						answer.append(""" """.join(_lowerCamelCase   ) + (remaining_spaces + 1) * """ """   )
						return answer


if __name__ == "__main__":
	from doctest import testmod

	testmod()







"""simple docstring"""





import unittest

from datasets import load_dataset

from transformers.pipelines import pipeline
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch, slow






@is_pipeline_test
@require_torch
class 	a		(   unittest.TestCase						):






    @require_torch
    def   UpperCamelCase			(			self	:				str   )	->						str:
      lowerCamelCase_       	=		pipeline(
          task='zero-shot-audio-classification'			,		model='hf-internal-testing/tiny-clap-htsat-unfused'   )
      lowerCamelCase_       	=		load_dataset('ashraq/esc50'   )
      lowerCamelCase_       	=		dataset['train']['audio'][-1]['array']
      lowerCamelCase_       	=		audio_classifier(__SCREAMING_SNAKE_CASE			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']   )
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[{'score': 0.501, 'label': 'Sound of a dog'}, {'score': 0.499, 'label': 'Sound of vaccum cleaner'}]			,		)






    @unittest.skip('No models are available in TF'   )
    def   UpperCamelCase			(			self	:				Tuple   )	->						Optional[Any]:
      pass






    @slow
    @require_torch
    def   UpperCamelCase			(			self	:				Optional[Any]   )	->						Optional[Any]:
      lowerCamelCase_       	=		pipeline(
          task='zero-shot-audio-classification'			,		model='laion/clap-htsat-unfused'			,		)
      # This is an audio of a dog
      lowerCamelCase_       	=		load_dataset('ashraq/esc50'   )
      lowerCamelCase_       	=		dataset['train']['audio'][-1]['array']
      lowerCamelCase_       	=		audio_classifier(__SCREAMING_SNAKE_CASE			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']   )

      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[
              {'score': 0.999, 'label': 'Sound of a dog'},
              {'score': 0.001, 'label': 'Sound of vaccum cleaner'},
          ]			,		)

      lowerCamelCase_       	=		audio_classifier([audio] * 5			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']   )
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[
              [
                  {'score': 0.999, 'label': 'Sound of a dog'},
                  {'score': 0.001, 'label': 'Sound of vaccum cleaner'},
              ],
          ]
          * 5			,		)
      lowerCamelCase_       	=		audio_classifier(
          [audio] * 5			,		candidate_labels=['Sound of a dog', 'Sound of vaccum cleaner']			,		batch_size=5   )
      self.assertEqual(
          nested_simplify(__SCREAMING_SNAKE_CASE   )			,		[
              [
                  {'score': 0.999, 'label': 'Sound of a dog'},
                  {'score': 0.001, 'label': 'Sound of vaccum cleaner'},
              ],
          ]
          * 5			,		)






    @unittest.skip('No models are available in TF'   )
    def   UpperCamelCase			(			self	:				Optional[int]   )	->						int:
      pass




